Evolutionary Optimization Versus Particle Swarm Optimization: Philosophy and Performance Differences
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
Nonlinear inertia weight variation for dynamic adaptation in particle swarm optimization
Computers and Operations Research
Population structure and particle swarm performance
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Population structure and particle swarm performance
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Application of Fuzzy Logic to Approximate Reasoning Using Linguistic Synthesis
IEEE Transactions on Computers
An improved GA and a novel PSO-GA-based hybrid algorithm
Information Processing Letters
Multi-population cooperative particle swarm optimization
ECAL'05 Proceedings of the 8th European conference on Advances in Artificial Life
Generating extended fuzzy basis function networks using hybrid algorithm
FSKD'05 Proceedings of the Second international conference on Fuzzy Systems and Knowledge Discovery - Volume Part I
Integrating fuzzy knowledge by genetic algorithms
IEEE Transactions on Evolutionary Computation
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Model-based recurrent neural network for modeling nonlinear dynamicsystems
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A hybrid of genetic algorithm and particle swarm optimization for recurrent network design
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Identification and control of dynamic systems using recurrent fuzzy neural networks
IEEE Transactions on Fuzzy Systems
Fuzzy tracking control design for nonlinear dynamic systems via T-S fuzzy model
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
A recurrent self-organizing neural fuzzy inference network
IEEE Transactions on Neural Networks
Identification and control of dynamical systems using neural networks
IEEE Transactions on Neural Networks
Memory neuron networks for identification and control of dynamical systems
IEEE Transactions on Neural Networks
Study on multi-depots vehicle scheduling problem and its two-phase particle swarm optimization
ICIC'09 Proceedings of the Intelligent computing 5th international conference on Emerging intelligent computing technology and applications
Symbiotic multi-swarm PSO for portfolio optimization
ICIC'09 Proceedings of the Intelligent computing 5th international conference on Emerging intelligent computing technology and applications
An improved two-stage camera calibration method based on particle swarm optimization
ICIC'09 Proceedings of the Intelligent computing 5th international conference on Emerging intelligent computing technology and applications
Structural and Multidisciplinary Optimization
Automatic rule tuning of a fuzzy logic controller using particle swarm optimisation
AICI'10 Proceedings of the 2010 international conference on Artificial intelligence and computational intelligence: Part II
MPSO-Based operational conditions optimization in chemical process: a case study
AICI'12 Proceedings of the 4th international conference on Artificial Intelligence and Computational Intelligence
Computers in Biology and Medicine
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Inspired by the phenomenon of symbiosis in natural ecosystems a multi-swarm cooperative particle swarm optimizer (MCPSO) is proposed as a new fuzzy modeling strategy for identification and control of non-linear dynamical systems. In MCPSO, the population consists of one master swarm and several slave swarms. The slave swarms execute particle swarm optimization (PSO) or its variants independently to maintain the diversity of particles, while the particles in the master swarm enhance themselves based on their own knowledge and also the knowledge of the particles in the slave swarms. With four benchmark functions, MCPSO is proved to have better performance than PSO and its variants. MCPSO is then used to automatically design the fuzzy identifier and fuzzy controller for non-linear dynamical systems. The proposed algorithm (MCPSO) is shown to outperform PSO and some other methods in identifying and controlling dynamical systems.